KardiaSoft Architecture – A Software Supporting Diagnosis and Therapy Monitoring of Heart Failure Patients Exploiting Saliva Biomarkers

Abstract

The aim of this work is to present the architecture of the KardiaSoft software, a clinical decision support tool allowing the healthcare professionals to monitor patients with heart failure by providing useful information and suggestions in terms of the estimation of the presence of heart failure (heart failure diagnosis), stratification-patient profiling, long term patient condition evaluation and therapy response monitoring. KardiaSoft is based on predictive modeling techniques that analyze data that correspond to four saliva biomarkers, measured by a point-of-care device, along with other patient's data. The KardiaSoft is designed based on the results of a user requirements elicitation process. A small clinical scale study with 135 subjects and an early clinical study with 90 subjects will take place in order to build and validate the predictive models, respectively. I. INTRODUCTION Heart Failure (HF) is a common clinical syndrome that affects approximately 2% of the adult population, ranging from less than 2% in population younger than 60 years old to 10% at ages over 75 years. Patients with HF have a poor prognosis with high short-and long-term mortality, high rehospitalization rates and severely impaired quality of life [1-3]. *Research supported by the KardiaTool project (http://www.kardiatool.eu/) that has received funding from the European Union's Horizon 2020 research and innovation program under the grant agreement No 768686. This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains.

Salivary Biomarkers for Diagnosis and Therapy Monitoring in Patients with Heart Failure. A Systematic Review

“…Also towards that direction, a research project is ongoing, aiming to create a novel point-of-care device that will be able to measure multiple saliva biomarkers (NT-proBNP, cortisol and possibly also TNF–α and IL-10) in HF patients and non-HF patients with risk factors such as obesity or hypertension. A software platform, based on machine learning techniques, might allow the extraction of patterns and rules towards efficient HF diagnosis and therapy monitoring in real time [ 64 ]. The use of saliva that is easy to acquire in any setting, in combination with a point-of-care device, are expected to render HF diagnosis and therapy monitoring feasible in primary health care or even at home, leading to a great cost reduction both for the health system and the patients.…”